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Found 39 papers, 20 papers with code
Echo: A Large Language Model with Temporal Episodic Memory
Research on large language models (LLMs) has shown remarkable performance in domains such as mathematics, programming, and literary creation.
Un-evaluated Solutions May Be Valuable in Expensive Optimization
Compared to mainstream SAEAs and Bayesian optimization algorithms, our approach incorporating the un-evaluated solution strategy shows a marked improvement.
LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch
Namely, the accuracy of most current LLM-based methods and the generality of optimization problem types that they can model are still limited.
Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models
Large Vision-Language Models (LVLMs) have achieved impressive performance, yet research has pointed out a serious issue with object hallucinations within these models.
Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning
Empathetic response generation endows agents with the capability to comprehend dialogue contexts and react to expressed emotions.
It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
To address these challenges, we formalize model merging as a multi-objective optimization problem and propose an automated optimization approach named MM-MO.
ORCDF: An Oversmoothing-Resistant Cognitive Diagnosis Framework for Student Learning in Online Education Systems
This paper contends that the oversmoothing issue arises from that existing CDMs seldom utilize response signals on exercises in the learning part but only use them as labels in the assessing part.
Large Language Models as Surrogate Models in Evolutionary Algorithms: A Preliminary Study
Specifically, we formulate model-assisted selection as a classification and regression problem, utilizing LLMs to directly evaluate the quality of new solutions based on historical data.
A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms
We employ KANs for regression and classification tasks, focusing on the selection of promising solutions during the search process, which consequently reduces the number of expensive function evaluations.
DOP: Diagnostic-Oriented Prompting for Large Language Models in Mathematical Correction
Math world problems correction(MWPC) is a novel task dedicated to rectifying reasoning errors in the process of solving mathematical problems.
Context-aware Diversity Enhancement for Neural Multi-Objective Combinatorial Optimization
Multi-objective combinatorial optimization (MOCO) problems are prevalent in various real-world applications.
Expensive Multi-Objective Bayesian Optimization Based on Diffusion Models
In this paper, we propose a novel Composite Diffusion Model based Pareto Set Learning algorithm, namely CDM-PSL, for expensive MOBO.
Inductive Cognitive Diagnosis for Fast Student Learning in Web-Based Online Intelligent Education Systems
To this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for fast new students' mastery levels inference in WOIESs.
Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings.
Enhancing Depression-Diagnosis-Oriented Chat with Psychological State Tracking
Depression-diagnosis-oriented chat aims to guide patients in self-expression to collect key symptoms for depression detection.
Wasserstein Differential Privacy
We propose Wasserstein differential privacy (WDP), an alternative DP framework to measure the risk of privacy leakage, which satisfies the properties of symmetry and triangle inequality.
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models
Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks.
Symbolic Cognitive Diagnosis via Hybrid Optimization for Intelligent Education Systems
The SCD framework incorporates the symbolic tree to explicably represent the complicated student-exercise interaction function, and utilizes gradient-based optimization methods to effectively learn the student and exercise parameters.
Mathematical Language Models: A Survey
In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics.
Interpretable Knowledge Tracing via Response Influence-based Counterfactual Reasoning
Knowledge tracing (KT) plays a crucial role in computer-aided education and intelligent tutoring systems, aiming to assess students' knowledge proficiency by predicting their future performance on new questions based on their past response records.
Unlearning with Fisher Masking
Experiments on various datasets and network structures show the effectiveness of the method: without any fine-tuning, the proposed Fisher masking could unlearn almost completely while maintaining most of the performance on the remain data.
Evolutionary Retrosynthetic Route Planning
The experimental results show that, in comparison to the Monte Carlo tree search algorithm, EA significantly reduces the number of calling single-step model by an average of 53. 9%.
Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization
Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to significantly enhance the efficiency of the algorithm.
EduChat: A Large-Scale Language Model-based Chatbot System for Intelligent Education
Currently, EduChat is available online as an open-source project, with its code, data, and model parameters available on platforms (e. g., GitHub https://github. com/icalk-nlp/EduChat, Hugging Face https://huggingface. co/ecnu-icalk ).
Scaling Multi-Objective Security Games Provably via Space Discretization Based Evolutionary Search
Then, a many-objective EA is used for optimization in the low-dimensional discrete solution space to obtain a well-spaced Pareto front.
SR-Forest: A Genetic Programming based Heterogeneous Ensemble Learning Method
Ensemble learning methods have been widely used in machine learning in recent years due to their high predictive performance.
Rethinking the Learning Paradigm for Dynamic Facial Expression Recognition
Dynamic Facial Expression Recognition (DFER) is a rapidly developing field that focuses on recognizing facial expressions in video format.
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Dynamic Facial Expression Recognition
Facial Expression Recognition
LMR-CBT: Learning Modality-fused Representations with CB-Transformer for Multimodal Emotion Recognition from Unaligned Multimodal Sequences
Learning modality-fused representations and processing unaligned multimodal sequences are meaningful and challenging in multimodal emotion recognition.
An Evolutionary Forest for Regression
Random forest (RF) is a type of ensemble-based machine learning method that has been applied to a variety of machine learning tasks in recent years.
A cross-modal fusion network based on self-attention and residual structure for multimodal emotion recognition
Firstly, we perform representation learning for audio and video modalities to obtain the semantic features of the two modalities by efficient ResNeXt and 1D CNN, respectively.
EvoGAN: An Evolutionary Computation Assisted GAN
Quantitative and Qualitative results are presented on several compound expressions, and the experimental results demonstrate the feasibility and the potential of EvoGAN.
Ensemble Defense with Data Diversity: Weak Correlation Implies Strong Robustness
In this paper, we propose a framework of filter-based ensemble of deep neuralnetworks (DNNs) to defend against adversarial attacks.
Variable Division and Optimization for Constrained Multiobjective Portfolio Problems
For example, a variable of portfolio problem can be divided into two partial variables, i. e. the selection of assets and the allocation of capital.
Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray
In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention.
Boundary Guidance Hierarchical Network for Real-Time Tongue Segmentation
Finally, the proposed hybrid loss in a four hierarchy-pixel, patch, map and boundary guides the network to effectively segment the tongue regions and accurate tongue boundaries.
Utilizing Dependence among Variables in Evolutionary Algorithms for Mixed-Integer Programming: A Case Study on Multi-Objective Constrained Portfolio Optimization
Consequently, we point out one challenge, faced by a direct coding scheme for MOEAs, to this problem.
Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization
Finally it uses the classifier to filter the unpromising candidate offspring solutions and choose a promising one from the generated candidate offspring set for each parent solution.
Learning from Non-Stationary Stream Data in Multiobjective Evolutionary Algorithm
Evolutionary algorithms (EAs) have been well acknowledged as a promising paradigm for solving optimisation problems with multiple conflicting objectives in the sense that they are able to locate a set of diverse approximations of Pareto optimal solutions in a single run.
